System and method for sentiment analysis of chat ghost typing

ABSTRACT

The present invention allows for the capture and sentiment analysis of text the customer inputs into a chat, but never actually sends to the customer service representative (ghost text). The system captures this ghost text with a ghost capture system (GCS) software module. The GCS module analyzes the ghost text to generate metadata. The ghost text and metadata are used by a sentiment analysis engine to apply appropriate sentiment to the ghost text. The sentiment and ghost text are routed to a customer service representative (CSR). This provides the customer service agent with additional detail and information about a customer&#39;s emotions during a text chat conversation, allowing the CSR to determine a court of interaction not only based on the customer&#39;s response, but also based on the ghost text and the sentiment from the ghost text.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/154,328, filed Oct. 8, 2018, the content of which is incorporatedherein by reference in its entirety.

FIELD

The present disclosure is directed to a method for computer analysis.Specifically, the present disclosure is direct to a method of analyzinginformation captured from text chat and analyzing the sentiment of thechat text.

BACKGROUND

In modern high-volume customer engagement centers (CEC), there are anumber of ways communication between a customer service representative(CSR) and a customer can take place. Communication in a CEC can takeplace over the phone, through email, and text chat, among other knowncommunication methods. When talking to a customer on the phone, the CSRcan detect the customer's emotions by the way in which the customerspeaks, the tone of voice, and the customer's choice of words. Byunderstanding the customer's emotions, the CSR is able to change his/herbehavior or take actions in an attempt to make an upset customer happy.

Increasingly in such CECs, interactions with customers are handledthrough text chat or voice chat transcribed to text. In theseinteractions CSRs have less insight into the customer's emotions. Textanalytics can provide some insight into the customer's emotions byexamining the language used. However, text analytics can only analyzethe text the customer chooses to send through the chat system. Valuableinformation such as how fast the customer types, how long the customerpauses between typing, and the text that a customer types into the chat,but deletes and never actually sends (ghost messages) is never capturedand analyzed.

In general, the current method for performing sentiment analysis on chattext is to capture the entire message a customer sends. The entiremessage is then analyzed for sentiment and a sentiment analysis ispresented to the customer service representative along with the messagesent. The customer service representative might use the sentimentinformation to change how they interact with the customer by making adifferent offer, changing the words used in the interaction, orescalating the matter to a supervisor.

However, as indicated above, the sentiment of a customer can only beanalyzed by the message that is actually sent. There is an unmet need tobe able to capture and analyze the information a customer servicerepresentative does not normally have access to such as how fast acustomer types a reply, how long the customer pauses before continuingto type, the messages a customer types, but then deletes and neversends. All of this information and ghost messages could be analyzed forsentiment and then provide a customer service representative with abetter depiction of a customer's actual sentiment, rather than just thesurface sentiment.

SUMMARY

The present invention allows for the capture and sentiment analysis oftext the customer inputs into a chat, but never actually sends to thecustomer service representative (ghost text). The system captures thisghost text with a ghost capture system (GCS) software module. The GCSmodule analyzes the ghost text to generate metadata. The ghost text andmetadata are used by a sentiment analysis engine to apply appropriatesentiment to the ghost text. The sentiment and ghost text are routed toa customer service representative (CSR). This provides the customerservice agent with additional detail and information about a customer'semotions during a text chat conversation, allowing the CSR to determinea course of interaction not only based on the customer's response, butalso based on the ghost text and the sentiment from the ghost text.

An embodiment of the present application is a method for capturing andanalyzing ghost text for sentiment. Incoming ghost text from outside acustomer engagement center (CEC) system is captured and analyzed using aghost capture service (GCS) software module in a GCS unit. Based on theanalysis of the GCS software module, the GCS software module generatesghost text metadata for the ghost text, which the GCS unit passes, alongwith the ghost text, to a sentiment analysis engine (SAE). The SAEperforms an analysis of the ghost text using a SAE software module.Based on the analysis, the SAE assigns at least one sentiment to theghost text. The SAE unit passes the sentiment, ghost text, and ghosttext metadata to a routing engine. The routing engine determines whichcustomer service representative (CSR) is to receive the sentiment andpasses the sentiment for display on a CEC desktop.

Another embodiment of the present application is a system for captureand analysis of ghost text. The system includes a processor and anon-transitory computer readable medium programmed with computerreadable code that upon execution by the processor causes the processorto execute the above-mentioned method for capture and analysis of ghosttext.

Another embodiment of the present application is a non-transitorycomputer readable medium programmed with computer readable code thatupon execution by a processor causes the processor to execute theabove-mentioned method for capture and analysis of ghost text.

The objects and advantages will appear more fully from the followingdetailed description made in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWING(S)

FIG. 1 depicts an exemplary embodiment of a customer engagement centersystem for capturing and analyzing ghost text for sentiment.

FIGS. 2a, 2b and 2c depict a flowchart of an exemplary embodiment of amethod for capture and sentiment analysis of ghost text.

FIG. 3 depicts an exemplary embodiment of a system for capture andsentiment analysis of ghost text.

DETAILED DESCRIPTION OF THE DRAWING(S)

In the present description, certain terms have been used for brevity,clearness and understanding. No unnecessary limitations are to beapplied therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes only and are intended to bebroadly construed. The different systems and methods described hereinmay be used alone or in combination with other systems and methods.Various equivalents, alternatives and modifications are possible withinthe scope of the appended claims. Each limitation in the appended claimsis intended to invoke interpretation under 35 U.S.C. § 112, sixthparagraph, only if the terms “means for” or “step for” are explicitlyrecited in the respective limitation.

CEC systems allow CSRs to engage with customers through a variety ofmanners. One of those ways is through text chat. By capturing generallyunseen and unused information from a text chat system and analyzing itfor customer sentiment, customer service representatives will receivebetter sentiment analysis and greater insight into the customer'scurrent emotions. Some of this information may include the speed atwhich the customer is typing, the amount of time a customer stops typingfor, the text a customer types, but then deletes and never sends. Thisinformation is typically not captured by text chat system and, istherefore never analyzed. Further, by logging and saving the text chat,ghost text, and sentiment analysis, the information can be furtheranalyzed to determine what practices are best at changing a customer'ssentiment from negative to positive or vice versa. This analysis can beused to assist customer service representative training and systemlearning for the future.

In embodiments, it is desirable for the system to capture and analyzeall text entered by the customer, even if the customer does not actuallysend the information to the customer service representative. Thiscaptured text is called ghost text. This allows the system to providethe CSR with better sentiment analysis of the customer's emotions. In anembodiment, ghost text is captured after the expiration of a specifictime period has elapsed. As a non-limiting example, the text written maybe captured every three seconds regardless of whether the customer sendsa message or not. In another embodiment, the ghost text may be capturedevery time a customer presses the space bar. It should be understoodthat these are only examples of how ghost text might be captured andshould not be construed as limiting. In further embodiments, it isdesirable for the system to capture and analyze the ghost text and allstatistical information about the ghost text, for example, but notlimited to, the speed at which the customer typed the ghost text, thetime the customer paused during typing, the number of edits, deletes, ormistakes made. In another embodiment, it is desirable for the system tolog the ghost text, the chat conversation, and the sentiment analysis.This permits the system to document all decisions made with regard tothe ghost text and sentiment analysis received and can assist indetermining what actions can cause a customer's sentiment to change fromnegative to positive or vice versa. In another embodiment, it isdesirable to display the customer's sentiment alongside the ghost textas it occurs to provide the customer service representative withprogressive updates as the representative waits for the customer'sresponse. In another embodiment, it is desirable to provide the customerservice representative with only a single sentiment analysis cumulatedbased off of all ghost text that occurred between sending responses.

FIG. 1 depicts an exemplary embodiment of CEC system 100 for capturingand analyzing ghost text.

CEC system 100 includes a ghost capture service (GCS) 110 having a GCSsoftware module 111 and an optional GCS storage 112. GCS 110 may be aprocessor or a combination of a processing system and a storage system.GCS unit 110 captures ghost text 120 from outside the CEC system 100 andprocesses the information using GCS software module 111 to generateghost text metadata 121. Ghost text 120 may be text or other information(images, pictures, etc.) input into a text chat by a customer that mayor may not be transmitted by the customer to the CSR, including but notlimited to, text that is typed and then deleted or changed beforesending. Optionally, the GCS unit 110 may also pass a copy of ghost textand ghost text data 120 and/or ghost text metadata 121 to internal orexternal GCS storage 112 for permanent or temporary storage.

Ghost text metadata 121 may include, but is not limited to, the speed atwhich the customer types, the time the customer spends not typing, thenumber of edits, deletes or mistakes made prior to sending a message(not shown). By way of a non-limiting example, the ghost text mayinclude where a customer types “this is”, then pauses for 3 seconds,deletes “is”, types “was”, pauses for 3 more seconds, types “a waste ofmy time”, pauses for 30 seconds, deletes “a waste of my time”, types“much more helpful than I thought it would be”, sends message.

CEC system 100 also includes a sentiment analysis engine (SAE) 130having a SAE software module 131 and an optional SAE storage 132. SAEmay be a processor or a combination of a processing system and a storagesystem. SAE 130 receives ghost text 120 with ghost text metadata 121from GCS unit 110 and analyzes it using SAE software module 131 todetermine the sentiment 139 of the ghost text 120 based on sentimentcriteria 133 within the SAE software module 131. Optionally, SAE 130 mayalso pass a copy of ghost text 120 and/or ghost text metadata 121 tointernal or external SAE storage for permanent or temporary storage.Stored ghost text 120 and/or ghost text metadata 121 may allowlarge-scale analysis of ghost text sentiment and trends.

Sentiment criteria 133 include rules conditioned on ghost text 120analysis and ghost text metadata 121. Sentiment criteria 133 may bedynamically updated by a CSR or another party as any of the criteriachanges. Depending on ghost text 120 analysis, ghost text metadata 121,and sentiment criteria 133, SAE will determine the sentiment 139 of theghost text 120. By way of non-limiting example, using the example abovewhere the customer starts to type “this was a waste of my time”, butthen deletes and types “this was much more helpful than I thought itwould be,” and then sends that message, the SAE may assign the firstghost text 120 a negative sentiment 139, whereas the second ghost text120 (which was ultimately sent as a message 129) may get a positivesentiment 139.

CEC system 100 also includes at least one CEC desktop 140 used by theCSR for viewing messages, ghost text 120, sentiment 139, and optionallyghost text metadata 121. CEC desktop 140 may also receive input forupdating sentiment criteria 133.

CEC system 100 also includes at least one routing engine 150 having arouting engine software module 151 and an optional routing enginestorage 152. Routing engine 150 may be a processor or a combination of aprocessing system and a storage system. Routing engine unit 150 receivessent messages 129, sentiment 139, ghost text 120, and/or ghost textmetadata 121 and uses routing engine software module 151 to route thesent messages 129, sentiment 139, ghost text 120, and/or ghost textmetadata 121. Optionally, routing engine 150 may also pass a copy ofsent messages 129, sentiment 139, ghost text 120, and/or ghost textmetadata 121 to internal or external routing engine storage 152 forpermanent or temporary storage. Stored sent messages 129, sentiment 139,ghost text 120, and/or ghost text metadata 121 may allow large-scaleanalysis of ghost text and trends.

FIGS. 2a-2c depict a flowchart of an exemplary embodiment of method 200for capture and sentiment analysis of ghost text.

Referring to FIG. 2a , in step 202, the GCS unit receives captured ghosttext from outside the CEC system in a chat session. In step 204, the GCSunit performs an analysis of the captured ghost text using the GCSsoftware module. The analysis may evaluate the captured ghost textcontent, existing attached metadata, header data, and images. In step206, the GCS unit generates ghost text metadata for the captured ghosttext based on the analysis of step 204. In step 208, the GCS unit passesthe captured ghost text and the associated ghost text metadata to a SAE.Optionally, the GCS unit may also pass the ghost text and/or associatedghost text metadata to GCS storage. In step 210, the SAE performs ananalysis of the captured ghost text and the associated ghost textmetadata using a SAE software module. The analysis is based on sentimentcriteria in the SAE software module. In step 212, the SAE assignssentiment to the captured ghost text based on the analysis of step 210.

Referring to FIG. 2b , in step 214, the SAE passes the sentiment, ghosttext, and optionally ghost text metadata to the routing engine forrouting. Optionally the SAE may also pass the sentiment, ghost text, andghost text metadata to SAE storage. In step 216 the routing enginedetermines the appropriate CSR to receive the sentiment based on therouting engine software module. In step 218 the routing engine passesthe sentiment to the determined CSR from step 216. Optionally, therouting engine may also pass the sentiment, ghost text, and ghost textmetadata to the routing engine storage. In optional step 220 the routingengine passes the ghost text to the determined CSR from step 216. Thisstep may occur simultaneously with step 218. Optionally, the routingengine may also pass the ghost text to the routing engine storage. Inoptional step 222 the routing engine passes the ghost text metadata tothe determined CSR from step 216. This step may occur simultaneouslywith step 218 and 220. Optionally, the routing engine may also pass theghost text metadata to the routing engine storage. In step 224, the CECdesktop displays the sentiment for CSR review.

Referring to FIG. 2c , in optional step 226, the CEC desktop displaysthe ghost text for CSR review. In certain embodiments, the step mayoccur simultaneously with step 224 or before step 224. In optional step228, the CEC desktop displays the ghost text metadata for CSR review. Incertain embodiments, the step may occur simultaneously with steps 224and 226 or before steps 224 and 226. In optional step 230, the CECsystem repeats steps 202 through 228 to continue capturing and analyzingghost text. This allows the CSR to be provided with ghost text andanalysis thereof until no ghost text remains. In step 232, the CECsystem transmits a sent message from the customer to the CSR.

FIG. 3 depicts an exemplary embodiment of a system 300 for capturing andanalyzing ghost text.

System 300 is generally a computing system that includes a processingsystem 306, a storage system 304, software 302, a communicationinterface 308, and a user interface 310. Processing system 306 loads andexecutes software 302 from the storage system 304, including a softwaremodule 320. When executed by computing system 300, software module 320directs the processing system 306 to operate as described in herein infurther detail in accordance with the method 200.

Computing system 300 includes three software modules 320 for performingthe functions of GCS software module 111, SAE software module 121, androuting engine software module 151. Although computing system 300 asdepicted in FIG. 3 includes three software modules 320 in the presentexample, it should be understood that one or more modules could providethe same operation. Similarly, while the description as provided hereinrefers to a computing system 300 and a processing system 306, it is tobe recognized that implementations of such systems can be performedusing one or more processors, which may be communicatively connected,and such implementations are considered to be within the scope of thedescription. It is also contemplated that these components of computingsystem 300 may be operating in a number of physical locations.

The processing system 306 can comprise a microprocessor and othercircuitry that retrieves and executes software 302 from storage system304. Processing system 306 can be implemented within a single processingdevice but can also be distributed across multiple processing devices orsub-systems that cooperate in existing program instructions. Examples ofprocessing systems 306 include general purpose central processing units,application specific processors, and logic devices, as well as any othertype of processing device, combinations of processing devices, orvariations thereof.

The storage system 304 can comprise any storage media readable byprocessing system 306, and capable of storing software 302. The storagesystem 304 can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Storage system 304 can be implemented asa single storage device but may also be implemented across multiplestorage devices or sub-systems. Storage system 304 can further includeadditional elements, such a controller capable of communicating with theprocessing system 306.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto store the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. Storage media may be internal or external to system 300.

User interface 310 can include one or more CEC desktops 140, a mouse, akeyboard, a voice input device, a touch input device for receiving agesture from a user, a motion input device for detecting non-touchgestures and other motions by a user, and other comparable input devicesand associated processing elements capable of receiving user input froma user. Output devices such as a video display or graphical display candisplay ghost text 120, ghost text metadata 121, messages 129, sentiment139, CEC desktop 140, or another interface further associated withembodiments of the system and method as disclosed herein. Speakers,printers, haptic devices and other types of output devices may also beincluded in the user interface 310. A CSR or other staff can communicatewith computing system 300 through the user interface 310 in order toview ghost text 120, ghost text metadata 121, sent messages 129,sentiment 139, update sentiment criteria 133, enter client input, managean interaction, or any number of other tasks the CSR or other staff maywant to complete with computing system 300.

As described in further detail herein, computing system 300 receives andtransmits data through communication interface 308. In embodiments, thecommunication interface 308 operates to send and/or receive data, suchas, but not limited to, ghost text 120, ghost text metadata 121,messages 129, sentiment 139 to/from other devices and/or systems towhich computing system 300 is communicatively connected, and to receiveand process client input, as described in greater detail above. Theclient input can include ghost text 120, ghost text metadata 121,messages 129, sentiment 139, details about a request, work order orother set of information that will necessitate an interaction betweenthe client and the representative. Client input may also be madedirectly to the CSR, as described in further detail above.

In the foregoing description, certain terms have been used for brevity,clearness, and understanding. No unnecessary limitations are to beinferred therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes and are intended to be broadlyconstrued. The different configurations, systems, and method stepsdescribed herein may be used alone or in combination with otherconfigurations, systems and method steps. It is to be expected thatvarious equivalents, alternatives and modifications are possible withinthe scope of the appended claims.

What is claimed is:
 1. A method for capturing and analyzing ghost text,comprising: capturing a ghost text, wherein the ghost text is textentered by a customer and not sent to a customer service representative(CSR); generating ghost text metadata for the captured ghost text basedon an analysis of the captured ghost text; assigning at least onesentiment to the ghost text based on an analysis of the ghost textmetadata and a set of sentiment criteria; routing the at least onesentiment to a determined CSR based on a determination of the CSR by arouting engine; and displaying the sentiment on a CEC desktop for thedetermined CSR.
 2. The method of claim 1, wherein the ghost text iscaptured by a customer engagement center (CEC) system and furtherwherein the ghost text entered is entered by the customer from outsideof the CEC system.
 3. The method of claim 1, wherein the analysis toassign the at least one sentiment to the ghost text includes analysis ofthe captured ghost text.
 4. The method of claim 1, further comprisingdisplaying the captured ghost text for the determined CSR.
 5. The methodof claim 1, further comprising receiving an update to at least onesentiment criteria.
 6. The method of claim 1, further comprisingdisplaying the ghost text metadata for the determined CSR.
 7. The methodof claim 1, wherein the ghost text is captured at predeterminedintervals.
 8. A system for capturing and analyzing ghost text forsentiment, comprising: a processor, and a non-transitory computerreadable medium programmed with computer readable code that uponexecution by the processor causes the processor to: capture a ghosttext, wherein the ghost text is text entered by a customer and not sentto a customer service representative (CSR); generate ghost text metadatafor the captured ghost text based on an analysis of the captured ghosttext; assign at least one sentiment to the ghost text based on ananalysis of the ghost text metadata and a set of sentiment criteria;route the at least one sentiment to a determined CSR based on adetermination of the CSR by a routing engine; and display the sentimenton a CEC desktop for the determined CSR.
 9. The system of claim 8,wherein the ghost text is captured by a customer engagement center (CEC)system and further wherein the ghost text entered is entered by thecustomer from outside of the CEC system.
 10. The system of claim 8,wherein the analysis to assign the at least one sentiment to the ghosttext includes analysis of the captured ghost text.
 11. The system ofclaim 8, wherein the ghost text metadata is at least one of a speed atwhich the customer typed the ghost text, a time the customer spent notactively typing the ghost text, a number of edits, deletes or mistakesmade prior to capturing the ghost text, A tone of the ghost text, or anintent or meaning of the ghost text.
 12. The system of claim 8, whereinthe processor is further caused to receive an update to at least onesentiment criteria.
 13. The system of claim 8, wherein the processor isfurther caused to display the ghost text metadata for the determinedCSR.
 14. The system of claim 8, wherein the ghost text is captured atpredetermined intervals.
 15. A non-transitory computer readable mediumprogrammed with computer readable code that upon execution by aprocessor causes the processor to: capture a ghost text, wherein theghost text is text entered by a customer and not sent to a customerservice representative (CSR); generate ghost text metadata for thecaptured ghost text based on an analysis of the captured ghost text;assign at least one sentiment to the ghost text based on an analysis ofthe ghost text metadata and a set of sentiment criteria; route the atleast one sentiment to a determined CSR based on a determination of theCSR by a routing engine; and display the sentiment on a CEC desktop forthe determined CSR.
 16. The non-transitory computer readable medium ofclaim 15, wherein the ghost text is captured by a customer engagementcenter (CEC) system and further wherein the ghost text entered isentered by the customer from outside of the CEC system.
 17. Thenon-transitory computer readable medium of claim 15, wherein theanalysis to assign the at least one sentiment to the ghost text includesanalysis of the captured ghost text.
 18. The non-transitory computerreadable medium of claim 15, wherein the ghost text metadata is at leastone of a speed at which the customer typed the ghost text, a time thecustomer spent not actively typing the ghost text, a number of edits,deletes or mistakes made prior to capturing the ghost text, A tone ofthe ghost text, or an intent or meaning of the ghost text.
 19. Thenon-transitory computer readable medium of claim 15, wherein theprocessor is further caused to receive an update to at least onesentiment criteria.
 20. The non-transitory computer readable medium ofclaim 15, wherein the ghost text is captured at predetermined intervals.